Study of slope length (L) extraction based on slope streamline and the comparison of method results.

Slope length is an important factor in soil erosion modeling, and the reasonable automatic extraction of slope length is of great significance in soil erosion research. However, previous studies have mainly focused on the regional scale, and how to effectively extract slope length at the slope scale deserves further research. In this study, a slope length extraction algorithm based on slope streamlines method (SSM) is proposed for the slope length extraction problem in geomorphology, and it is compared with three existing slope length calculation methods. The experimental results show that the new method can quickly calculate the length of slope streamlines, and the extracted slope lengths have better accuracy; the coefficients of determination demonstrates a better overall fitting effect of the four extraction methods, with coefficients of determination exceeding 0.7; this indicates that the use of SSM has similar accuracy and stability to other methods in calculating slope lengths. Among all the calculation methods, SSM has a better overall fitting effect for slope length calculation, and the obtained slope length value domain range is relatively small and concentrated in a small range, which expresses the slope length better.

Modeling soil loss under rainfall events using machine learning algorithms.

Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km2 from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017-2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 104 t/(km2∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4-51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future.

A Multi-Criteria Framework for Identification of Gully Developmental Stages Based on UAV Data-A Case Study in Yuanmou County, Yunnan Province, SW China.

Gully erosion is a common form of soil erosion in dry-hot valleys, and it often brings serious land degradation. A multi-criteria method integrating the characteristics of the longitudinal profile (LP), the cross profile (CP) and the knickpoints of gullies was applied to identify the development stage of gullies in Yuanmou County, Yunnan Province, in southwestern China. Firstly, based on the high-resolution data sources produced by an unmanned aerial vehicle (UAV), 50 gullies were selected as the typical ones in Tutujiliangzi and Shadi village. The LPs were extracted, and their morphological indices, information entropy and fitting functions were calculated. The morphological characteristics of the CPs and the presence or absence of knickpoints were recorded. The results show that the period of the gullies in Tutujiliangzi and Shadi is dominated by the deep incision period and the equilibrium adjustment period, which means that most gullies are in the period of the severe erosion stage. Among the gullies, 13 LPs' morphological index is between 0.636 and 0.933, and the morphology of the LP presents an upward convex shape; the cross profiles are mainly V-shaped and U-shaped. Thirty-two LPs' morphological index is between 1.005~2.384, which presents a slightly concave shape; the cross profiles are mainly repeated U-shapes. The remaining five LPs have a morphological index of 0.592, 0.462, 1.061, 1.344 and 0.888, respectively; the LPs of upstream and downstream are different. The LPs of the Tutujiliangzi gullies are nearly straight lines and slightly concave, while those of the Shadi village gullies are convex and nearly straight lines. The knickpoints and step-pools in Shadi village are more developed, while the gullies in Tutujiliangzi develop more rapidly. This study shows that in counties with similar conditions, these conditions such as temperature and precipitation, local topographic changes, soil properties and vegetation conditions have obvious effects on the development of gullies.

[Distribution and enrichment characteristics of soil nutrients in the nebkhas profile of Nitraria tangutorum in Gahai Lake Area, Qaidam Basin.] / æŸ´è¾¾æœ¨ç›†åœ°å°•æµ·æ¹–åŒºç™½åˆºçŒä¸›æ²™å †å‰–é¢åœŸå£¤å »åˆ†çš„åˆ†å¸ƒå’Œå¯Œé›†ç‰¹å¾.

As a special bio-geomorphic landscape in the Qaidam desert area, Nitraria tangutorum nebkhas play a critical role in fixing quicksand, improving soil quality, and maintaining the stability of regional ecological environment. Taking the N. tangutorum nebkhas with coverage of approximately 15%, 25%, 45% and 60% in Gahai Lake area of Qaidam Basin as the research objects, we analyzed the vertical distribution and enrichment characteristics of soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP) and available potassium (AK). The results showed that the contents of SOM, TN, TP, TK, AN, AP and AK varied in the range of 1.67-10.22 g·kg-1, 0.05-0.42 g·kg-1, 0.31-0.54 g·kg-1, 15.87-18.84 g·kg-1, 2.26-11.68 mg·kg-1, 0.80-15.00 mg·kg-1 and 45-161 mg·kg-1, respectively. Vertically, soil nutrients in the N. tangutorum nebkhas with 15% coverage showed a decreasing trend first then increased, and then decreased again with the increase of soil depth, except for TP, which did not show any significant change. In the nebkhas with 25%, 45% and 60% coverage, SOM, TN, AN, TP and AP all showed a decreasing trend with increasing soil depth ,whereas TK and AK did not change significantly with soil layer. Above the nebkhas ground level of N. tangutorum, SOM, TN, TK, AN, AP and AK were all enriched, especially in the surface layer.Aamong all the nutrients, the enrichment rate of AN reached 5.19. In addition, below the nebkhas ground level of N. tangutorum, TN, AN, TK, AK and AP also showed enrichment. SOM, TN, AN, TP, AP, TK and AK were all significantly positively correlated with soil water content, and negatively correlated with altitude. All nutrients except TP were mainly affected by altitude. In conclusion, soil nutrient content of N. tangutorum nebkhas was the highest in the surface layer, the enrichment effect of which was not only reflected in the interior of the nebkhas, but also below the ground level of the nebkhas. Our results could provide reference for the scientific utilization of N. tangutorum nebkhas and ecological environment protection in Qaidam Basin area.